Databricks Machine Learning Associate Question 209
Single answerYou are working on a machine learning project in Databricks and considering parallelizing a sequential/iterative model, such as Gradient Descent or ARIMA. Why might parallelizing such models be challenging?
- A
Sequential/iterative models often rely on the output of previous steps to compute the next step.
- B
Databricks cannot distribute tasks across a cluster for iterative algorithms.
- C
The model training process in sequential algorithms requires large memory, and parallelization cannot handle such requirements.
- D
Parallelizing sequential models requires specialized libraries that are not compatible with Databricks.
Show answer and explanation
Correct answer: A
Explanation
The key challenge with parallelizing sequential/iterative models lies in their inherent dependency on previous steps for computation. Each step in the process relies on the outcome of the last, which makes it difficult to distribute the work across multiple nodes or cores. This is a limitation of the model's structure, not of Databricks or parallelization frameworks.
- A. Correct.
Sequential/iterative models rely on the output of previous steps to compute the next step, making it inherently difficult to parallelize such processes because the dependency between steps prevents distributing work across multiple nodes in parallel.
- B. Incorrect.
Databricks can indeed distribute tasks across a cluster, but the challenge lies in the inherent sequential nature of certain algorithms, not in Databricks' capabilities.
- C. Incorrect.
While sequential models may require significant memory, the difficulty in parallelizing them is not due to memory constraints but due to their dependency structure.
- D. Incorrect.
There are libraries that support parallelization in Databricks, but the core issue with sequential models is their dependency on previous steps, not a lack of compatible libraries.